Pregnancy in cardiac transplant recipients
Bibliographic record
Abstract
PURPOSE: Successful pregnancy following cardiac transplantation has been described, although outcome data from individual centers are relatively sparse. We investigated maternal and fetal outcomes including change in left ventricular (LV) function and calcineurin inhibitor (CNI) dose in women who became pregnant from our institution. METHODS: We identified every female patient <49 years at the time of transplant who survived >3 months post-surgery, between 1985 and 2014. Those who conceived had a review of their medical records and transplant charts. Those currently alive were interviewed. RESULTS: There were 22 pregnancies in 17 women with 20 live births (91%). Mean time from transplantation was 98±62.4 months. Rejection complicated one pregnancy, and LV function remained normal in all others. Hypertension complicated 3 (13.6%), preeclampsia 3 (13.6%), and cholestasis 1 (4.5%). Mean birthweight was 2447±608 grams at 34.1±3.6 weeks. Four women died following pregnancy. A significant increase in total daily dose of tacrolimus and cyclosporine A was required to maintain therapeutic levels through pregnancy (CyA, P<.001; and Tac, P=.001), with no deterioration in serum creatinine. CONCLUSIONS: We report a 91% live birth rate post-cardiac transplantation. Meticulous individualized care with frequent monitoring of CNI levels and LV function is necessary to optimize the maternal and fetal outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".